For many years, dark field scanning methodologies have been used to scan surfaces. Dark field scanning makes use of light scattered by the surface features to characterize and examine features of the surface. FIG. 1 is a cross-section view of an illuminated surface used to illustrate aspects of dark field scanning. An illumination source 101 projects a light beam I (also referred to herein as the incident beam) onto the surface 102 being examined. A portion of the incident beam I is reflected by the surface as the reflected beam R. If the surface 102 was perfectly reflective, the entire incident beam I would be reflected. However, most surfaces have a variety of characteristics which cause a portion of the light from an incident beam I to be scattered. Dark field scanning makes use of this scattered light.
One particular surface feature that causes light scattering is referred to as a defect. The detection and quantification of defects are important in many areas. In particular, defect detection and analysis are important in semiconductor processing. In semiconductor processing, defects are frequently scattering features. Such defects typically occur in only one of the many dies on a wafer. Consequently, their detection is aided by systems that can compare the scattering patterns from multiple dies on a wafer and identify features which occur only in an isolated die. This method is called die-to-die comparison. Defects include, but are not limited to, pits, bumps, scratches, and a number of other features which mar the surface 102. Thus, the light of an incident beam I is often subject to some degree of scattering. FIG. 1 illustrates a typical incident beam I having a light scattering pattern schematically depicted by a plurality of scattered light rays 103, 104, 105, 106, and 107 which are scattered by a surface defect 108.
The dark field method places a single discrete light detector (not shown) so that it is not in the path of the reflected beam R. Thus, the background (the field) is dark. The scattered light received by the detector provides a representation of the surface 102 whereby the surface defects show up as lighter regions against the dark background or field. Hence, the name dark field scanning.
FIG. 2 is another cross-section view of a surface being scanned using dark field scanning. The surface 102 is illuminated by an incident beam I, a portion of which is reflected as reflected beam R. Another portion of the light of the incident beam I is scattered. Here, the scattered light is schematically depicted by the scattered light rays S1, S2, and S3. Each of the scattered light rays S1, S2, and S3 have scattering angles associated therewith. Because FIG. 2 is a two-dimensional representation of a three-dimensional reality, only one scattering angle is depicted for each of the scattered light rays S1, S2, and S3. In the depiction of FIG. 2, the scattering angles are measured from the illuminated surface 102. Thus, scattered light ray S1 is associated with scattering angle A1. Scattered light ray S2 is associated with scattering angle A2. Scattered light ray S3 is associated with scattering angle A3, and so on. The scattering angles S1, S2, S3 depicted here are determined from the surface 102. However, scattering angles can be described using other methods and coordinate systems. For example, the scattering angles can be determined from a line normal to the surface 102.
FIG. 3 is a schematic three-dimensional view of an incident light beam I and a scattered light ray 301. The depicted coordinate system is an x, y, z coordinate system with the surface lying in the x-z plane. One scattering angle is depicted as φ, which is the angle from the x-z plane. The other depicted angle is θ, which is the angle from the y-z plane. As was previously stated, many other ways of referring to scattered light ray angles are known and can be used.
One type of conventional dark field surface inspection device 400 is depicted in FIG. 4. An ellipsoidal mirror 420 is positioned over an inspection surface 402. An incident light beam 401 is directed onto an inspection surface 402. Schematically depicted are a reflected light beam 403 and many scattered light beams 410, 411, 412, 413, 414, 415, and 416. The device includes a first single discrete photodetector element 421 (for example a PMT) and a second discrete photodetector element 422 positioned above the ellipsoidal mirror 420. A portion of the scattered light (depicted here by scattered light beams 410, 411, 412, 413, 414, 415, and 416) passes through an opening 0 in the ellipsoidal mirror 420. The center portion of the scattered light beams (schematically depicted by beams 415, 416) passes through a lens 423, which directs the light onto a central mirror 424, which reflects the central beams 415, 416 so they converge at a side focal point 425. The second discrete photodetector element 422 is positioned at the side focal point 425 to receive the central beams 415, 416. At the same time, an outer portion of the scattered light beams (schematically depicted by beams 410, 411, 412, 413, 414) passes through the opening in the ellipsoidal mirror 420 and is reflected by the ellipsoidal mirror 420 onto a top focal point 426. The ellipsoidal mirror 420 is specifically designed to concentrate the outer portion of the scattered light beams 410, 411, 412, 413, 414 onto the top focal point 426. Also, the first discrete photodetector element 421 is specifically positioned at the top focal point 426. Frequently, the discrete photodetector elements 421, 422 include optical fibers that convey the focused light to a single discrete photodetector element (commonly, a single photo multiplier tube (PMT)). Such conventional discrete photodetector elements are commonly very sensitive to light intensity, but have no way of generating two-dimensional images that characterize scattered light from the inspection surface. Other related conventional approaches use discrete photodetector elements without the added optical fiber. By integrating light information from the first discrete photodetector element 421 and the second discrete photodetector element 422, the presence of a defect can be determined.
A problem with such discrete photodetector element systems is that they have difficulty discerning defects in patterned surfaces. Frequently, when patterned surfaces (e.g., the patterned surfaces of semiconductor wafers) are scanned, the resulting scattering pattern is detected as a “defect” by the discrete photodetector element. Even in systems which employ die-to-die comparison, small variations in the surface pattern and the resulting variation in scattering can mislead the system into falsely identifying a defect. Thus, portions of the (otherwise defect-free) patterned surface give false readings, as if they had defects in the surface. Conventional devices have attempted to circumvent this problem by so-called Fourier filtering. Under plane wave illumination, the intensity distribution at the back focal plane of a lens is proportional to the Fourier transform of the object. Further, for a repeating pattern, the Fourier transform consists of a pattern of light areas which remain constant as the wafer is scanned. By placing a filter in the back focal plane of the lens, these areas can be blocked (filtered). Thus, artifacts of the repeating circuit pattern can be filtered out and leave only non-repeating signals from particles and other defects. Such Fourier filtering is a common technology employed in wafer inspection machines from many manufacturers.
One of the limitations of Fourier filtering based instruments is that they can only inspect areas with repeating patterns (for example, arrays of memory cells) or blank areas. Critically, Fourier filtering of the type describe is not useful for inspecting non-uniform surfaces like random logic areas. These are some fundamental limitations of the technology.
For example, in the Hitachi Model IS-2300 inspection machine, darkfield Fourier filtering is combined with die-to-die image subtraction to effectuate wafer inspection. Using this technique, non-repeating pattern areas on a wafer can be inspected by the die-to-die comparison. However, even with such die-to-die comparison, conventional technologies still need Fourier filtering to obtain good sensitivity in the repeating array areas. For example, in dense memory cell areas of a wafer, a darkfield signal from the circuit pattern is usually so much stronger than that from the circuit lines in the peripheral areas that the dynamic range of the sensors are exceeded. As a result, either small particles in the array areas cannot be seen due to saturation, or small particles in the peripheral areas cannot be detected due to insufficient signal strength. Fourier filtering equalizes the darkfield signal so that small particles can be detected in dense or sparse areas at the same time.
Common disadvantages to such techniques include the following limitations. First, such machines can detect particle defects relatively well, but their sensitivity to pattern defects is very poor. Second, since the filtered images are usually dark without circuit features, it is not possible to do an accurate die-to-die image alignment, which is necessary for achieving good cancellation in a subtraction algorithm. One solution is to use an expensive mechanical stage of very high precision, but even with such a stage, due to the pattern placement variations on the wafer and residual errors of the stage, the achievable sensitivity is limited roughly to particles that are 0.5 μm and larger. This limit comes from the alignment errors in die-to-die image subtraction. Additionally, the filtering makes it difficult to detect defects in certain regions of the surface. Moreover, as surface patterns become more complicated (as is the case in modern VLSI circuit structures), the patterns become more complex, and more filtering must be implemented. As a result, less and less of the surface can be effectively scanned for defects. Additionally, although Fourier filtering can be extremely effective in filtering light scattered by array areas (e.g., memory cells), there is currently no similar technique that can be applied to areas of the wafer where the surface pattern is not regular and repeating. Examples of such areas include random logic areas. In these areas, the scattering pattern at the Fourier transform plane of a lens is not constant as the wafer is scanned. As a result, it is no longer practical to insert a fixed filter to selectively block light scattering caused by the surface pattern.
What is needed are dark field inspection tools and methodologies that can achieve adaptive filtering of surface pattern effects and address some of the foregoing difficulties.